#TSAS is hosting a special issue on #UrbanMobility this Spring. Possibly of interest to those working with #MovingPandas and similar tools.

https://maestro.acm.org/trk/click?ref=z16l2snue3_2-318b0_0x343b59x05212

#MobilityData #Mobility #UrbanData #GeoScience

Machine learning turns volunteers’ lower-quality #OpenStreetMap data into quality indicators for official, authoritative data. Novel method and case studies accepted for publication in ACM Transactions on Spatial Algorithms and Systems (#TSAS).

Fighting fires, containing floods, or police missions, these and other situations require the availability of high-quality map data. Authoritative data exhibits such high quality at entry time, but expert geographers update it only every few years by scanning the whole environment, leading to many stale and misrepresented geographical regions. Volunteered map data, such as open street maps, contains many mistakes, as it is not captured by experts. However, volunteers are often very motivated to refresh map data quickly. We suggest to combine the best of both worlds and exploit machine learning to point experts to regions that need attention of data curation.

Insitute website: https://www.ki.uni-stuttgart.de/institute/news/Machine-learning-turns-volunteers-lower-quality-open-street-map-data-into-quality-indicators-for-official-authoritative-data./

Paper: https://dl.acm.org/doi/10.1145/3715910